Position Measurement Under Uncertainty Using Magnetic Field Sensing
| Autor: | Saeed Daroogheha, Bahram Ravani, Ty A. Lasky |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Magnetic moment
Magnetometer business.industry Computer science Noise (signal processing) 010401 analytical chemistry Robotics 02 engineering and technology Kalman filter 021001 nanoscience & nanotechnology 01 natural sciences 0104 chemical sciences Electronic Optical and Magnetic Materials law.invention Magnetic field Transformation (function) law Position (vector) Computer vision Artificial intelligence Electrical and Electronic Engineering 0210 nano-technology business |
| Zdroj: | IEEE Transactions on Magnetics. 54:1-8 |
| ISSN: | 1941-0069 0018-9464 |
| DOI: | 10.1109/tmag.2018.2873158 |
| Popis: | This paper presents a method for position measurement under uncertainty using magnetic sensing. The statistical transformation of magnetic field data in the presence of noise is first developed. An unscented Kalman filter is then formulated based on a stochastic dynamic model that would allow for position estimation from magnetic field sensing. Finally, applications of magnetic sensing-based positioning in robotics and vehicle guidance are provided to validate the algorithm. The methods presented in this paper extend filtering theory for extracting positioning information from magnetic fields. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|